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Companies are eager to leverage the benefits generative AI (GenAI) offers. Implementing the technology, however, isn't a 'plug-and-play' process. Instead, it hinges on a critical task — fine-tuning a pretrained large language model (LLM) to be a specialist at your intended domain or application.
Learn how to adopt a mindset of continuous evaluation in generative AI, exploring popular benchmarks and AI red teaming methods.
These 3 conversational AI best practices will help you identify and develop new solutions, like natural language search tools, tailored to business goals.
Discover the processes involved in field operations testing for enhancing advanced driver assistance systems and key considerations for program success.
Learn how to automate the evaluation and categorization of LLM attack methods so your AI red team ensures good test coverage and finds vulnerabilities.
How Shortcuts and App Intents pave the way for Apple Intelligence success on iOS 18
As GenAI implementations become more prominent, it's critical to adhere to responsible AI practices to protect your brand and foster customer trust.
Our industry takeaways from Apple's 2024 Worldwide Developers Conference (WWDC), including Apple Intelligence and Apple Vision Pro.
Can a chatbot be empathetic? Discover what empathetic chatbots look like, how're they're trained and the impact they could have on your business.
Learn how these five artificial intelligence design techniques build trust in highly regulated industries like healthcare.
Learn about some popular customer experience GenAI use cases, and how the technology can help CXM leaders maintain a competitive advantage.
Data pruning helps generative AI developers manage increasingly massive datasets for better performance, lower costs, and more useful analytics.
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